Healthcare Analytics Framework

Healthcare Analytics Framework MHA 605 Week 2 Discussion

Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Refer to the Discussion Forum Grading Rubric under the Settings icon above for guidance on how your discussion will be evaluated.

 Healthcare Analytics Framework

The use of healthcare analytics is an important step towards providing value to the transformation; this is taking place in the use of big data to assess areas of cost, innovation, productivity, and safety. These areas create value in the healthcare organizations as executives and leaders seek advanced decision-making metrics. Based on the aforementioned benchmarks the use of the Health Analytics Continuum and the Value Life Cycle will create increase in both data quantity and quality in any chosen hospital department (clinical, operations, or financial).

For this discussion you are to choose one area for the Healthcare Value Framework and one from the Value Life Cycle and discuss how each adds to the overall Healthcare Analytics Framework based on your chapter readings and current scholarly articles. Your discussion should be no more than 300 words in length.

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Healthcare Value Framework

The Healthcare Value Framework (Davenport, 2014).

Value Life Cycle

The Value Life Cycle (Davenport, 2014).

Healthcare Analytics Framework MHA 605 Week 2 Discussion

Healthcare Analytics Framework MHA 605 Week 2 Discussion Guided Response: Review your peers’ posts and provide a substantive response to at least two of your classmates’ posts by Day 7. A substantive response is a respectful, professional, and unique response that is at least five sentences in length and incorporates the following:

  • Highlights the key points of what you have learned from your peer’s post.
  • Adds your content knowledge.
  • Compares and contrasts.
  • Provides further research.
  • Is topic-related.

Monitor the forum through Day 7 to allow for robust dialogue.

Healthcare Analytics Framework MHA 605 Week 2 Discussion

In Davenport’s Healthcare Value Healthcare Analytics Framework MHA 605 Week 2 Discussion Framework (2014), analytics can play a huge role in Medical Cost Reduction.  Since medical costs represent the majority of an organization’s costs, this is the area where the most significant savings may be achieved (McNeill, 2014).  Evidence-based medicine (EBM) should be utilized to ensure the best and most cost-effective outcomes.  Analytics may help detect statistical outliers—for instance, physicians who order unnecessary, expensive tests or deviate from EBM.  (We have one neurologist who orders full spine MRIs on all his patients, whether justified or not.)  Also, analytics may detect and flag instances of duplicate test orders.  It is not uncommon for a hospital inpatient to have numerous doctors—for instance, a hospitalist, a cardiologist, and a pulmonologist.  All three of these physicians may order a chest x-ray on the same patient for the same day.  The system should detect this and warn them of (and/or disallow) the order duplication.  It may also be possible to use analytics to discover and control inventory waste.

In the Value Life Cycle, Discovery is the first step toward developing a business strategy that aligns the organization’s future operations with its mission and objectives.  This is where the standards and expectations are set.  Before analytics can be employed, the company’s objectives must be clearly defined.  What exactly does the organization value?  What outcomes does it hope to achieve?  How are these objectives prioritized?  What are the right steps to take toward meeting these goals?  These are the initial questions that must be asked if Business Intelligence is to properly serve the best interests of an organization.

Healthcare Analytics Framework MHA 605 Week 2 Discussion Reference

McNeill, D. (Ed). (2014). Analytics in healthcare and the life sciences: Strategies, implementation, methods, and best practices. Upper Saddle River, NJ: International Institute for Analytics, Pearson Publisher.